India JMP Users Group

Feb2024-userGroup-India-h v2.jpgHey there,   I would like to invite you to attend the JMP Indian User Group Meeting on 28 February 2024, at 1:30 – 2:35 pm New Delhi time. This meeting will be held online. Like many other JMP events, sharing knowledge and sparking creativity are always among our key objectives. At this meeting, Kim Hui Lim, a consultant from JMP, will provide an overview of all the JMP capabilities, with emphasizes on: Process and Quality - control chart and capability analysis Visualization - graph builder Automation - workflow builder   We have also selected two speakers from all those who submitted an abstract. Those two are: Prakash Muthudoss, Chief Scientific Officer, A2Z4R Research and Analytics Private Limited. He will give a presentation on ‘Machine Learning and Hyperparameter by Design (HyD)’. (30 minutes) Suresh Kumar Kommu, Associate Manager, Biocon Biologics India. His presentation is titled ‘Continued Process Verification (CPV) using JMP’. (30 minutes) This meeting will be end with a Q&A hosted by our meeting panelists: @Chandramouli_R Chandramouli Ramnarayanan, Global Technical Enablement Engineer, JMP, Bangalore  @lkimhui Kim Hui Lim, Regional Engineering Analytics Consultant, JMP, Singapore   Register via JMP website here, or use this link: https://www.jmp.com/en_in/events/live-webinars/non-series/ue-in-28feb2024.html  Feel free to share it with other JMP users. Looking forward to meeting you online. Best regards, Shuran Xu   Indian User Group Meeting  ... View more

Welcome to the India User Group Community Page!   At JMP, we are proud that our users are located all over the world. Apart from (of course) being super happy to see our software becoming even more successful after 30 years, we just love to know that people are working hard on complex research and engineering even in some of the most remote places on our planet.    We want to provide as many resources as possible to help you get better at using JMP. However, this is usually limited by geological location, time difference, and so on.   So we have created this page just for you Indian JMP users. The website is far from perfect yet, we know that and we are working on it. But the people here are real, and their years to decades of experiences are just so rare.   Here we will post about: Events for Indian users. Events that are available for people in India to attend. Industry/job specific articles to give you tips and tricks on using JMP. Webinars/White Papers/Seminar materials. And much more. Even though we gonna post materials here, this is still your community. Don't be afraid to ask questions, respond to posts, or get to know other JMP users from other Indian companies. There is no limit, but be truthful, mindful and respectful. If you read this far, thank you! धन्यवाद! Join the group to receive email notifications of the above materials. And don't forget to comment, share and subscribe XD Picture1.png ... View more

Shuran_0-1706687042959.jpeg The K Nearest Neighbours, or KNN, platform predicts a response value for a given observation using the responses of the observations in that observation’s local neighbourhood. It can be used to classify a categorical response as well as to predict a continuous response.   KNN is a non-parametric, supervised machine learning algorithm. It works on the underlying assumption that similarities in certain parameters (the observations) reflect similarities in other parameters (the prediction). To fulfil this assumption, KNN ranks the k smallest distances between the given observation and all other observations.   Because of this fact, K-Nearest Neighbours can classify observations with irregular prediction boundaries. However, as the algorithm is sensitive to irrelevant predictors, the choice of predictors can affect your results.   Calculate the distance JMP pro 17 uses Euclidean Distances to calculate the distances between the predictor values and those of others. In a 2D space, the Euclidean Distance d between (x1, y1) and (x2, y2) is simply: d = sqrt((x2-x1)^2 + (y2-y1)^2) the Euclidean method requires that all the values are real.   Making predictions The KNN gives its prediction depending on the types of responses it has. - Continuous responses JMP predicts values as the average of the responses of the k nearest neighbours. Each continuous predictor is scaled by its standard deviation. Missing values for a continuous predictor are replaced by the mean of that predictor. - Categorical responses In JMP, the predicted value is the most frequent response level for the k nearest neighbours. If there is more than one top response, the predicted response is assigned by randomly selecting one of these levels.   The k value or n_neighbors Different values of k can give different predictions. In JMP, you only need to specify the maximum number of k. For each k, 1 ≤ k ≤ k_max, JMP builds a model using only the training set observations. Each of these models is used to classify the validation set observations. The validation set results are used to select the model with the lowest misclassification rate (aka the best k).   Validation for KNN in JMP modelling You can add a numerical column as a validation for KNN modelling in JMP Pro. JMP uses validation to partition data into sets before modelling to reduce overfitting and select a good predictive model. For KNN, this feature is optional. However, overfitting can lead to a mismatch between training and prediction, i.e., the algorithm is overly specific to the training data, resulting in less accurate predictions. For KNN, you can partition the data into two or three sets by the following methods: Method Set Function Train and tune Training Estimate model parameters Validation Tune the model parameters in the model fitting algorithm, and ultimately choose a model with good predictive ability Train, tune, and evaluate Training Estimate the model parameters Validation Tune the model parameters in the model fitting algorithm, and ultimately choose a model with good predictive ability Test Independently evaluate the performance of the fitted model   Learn more about validation in the JMP Documentary: Example of the Make Validation Column Platform (jmp.com) Example In this example, we want to use KNN to categorise a customer between Good Risk and Bad Risk based on their financial history. Select Help > Sample Data Folder and open Equity.jmp. Shuran_1-1706687043066.png   Select Analyze > Predictive Modeling > K Nearest Neighbors. Shuran_2-1706687043265.png   Select BAD and click Y, Response. The is the value to analysis. Select LOAN and CLNO and click X, Factor. This is the predictor variables. Select Validation and click Validation. This is the value used to partition data into sets before modelling to avoid overfitting and select a good predictive model. We used Train, Tune, and Evaluate method in the example. Shuran_3-1706687043323.png   Click OK. Shuran_4-1706687043524.png   To save the prediction equation Right click the BAD red triangle and select Publish Prediction Formula. Next to Number of Neighbors, K, leave the default value of 7. Click OK. Shuran_5-1706687043571.png   The prediction equation is saved in the Formula Depot. You can compare the performance of alternative models published to the Formula Depot with that of the K = 7 nearest neighbour model using the Model Comparison option in the Formula Depot. Find more information regarding the Formula Depot in Help/JMP Documentation Library, page 3763. If you are interested, you can go back to the step 4. This time put all the parameters in between LOAN and CLNO as X, Factor. What changed and why?   Shuran, Reviewed by Jeremy T. @Jeremy_Tee  Jan 2024, Based on JMP(R) Pro 17.2.0   This article does not reflect the positions and opinions of JMP Statistical Discovery nor SAS Institute.   ... View more

I have been told by many different people that they spend 80% of their time preparing their data and 20% of their time doing the actual analysis. While the data analysis was 80% of the fun, the preparation was -81%. (and I was also told by an engineer that % can't be negative, so this is a bad joke XD)   In JMP, we provide many tools that make it easy to prepare your data, so you can spend 80% of your time analysing.   Data preparation For a quick start, here is a 2-min data transformation guide: Transforming Data - JMP User Community   The systematic approach to data preparation generally follows a two-step approach: 1) you determine a goal for the analysis, then 2) you can prepare the data to meet that goal. Watch this webinar by our JMP system engineers Olivia Lippincott and Alisa Howery to learn more. In this webinar, they use a case study on medical treatment types to demonstrate the approach: Preparing Your Data for Analysis - JMP User Community   Column formula It may be necessary to create a new column of data using the existing data. JMP has a column formula tool designed for this purpose. This formula tool can be found by right-clicking on the column header. Shuran_0-1705892707563.png Figure 1. Where is the column formula anyway (right click column header).   In the column formula panel, you can drag and use - Predefined mathematical and statistical equations. - Conditional expressions (e.g., for condition and if condition). - Random, probability and discrete probability functions. - Linear, interaction and full quadratic models. - Predefined financial models.   Shuran_1-1705892766550.png Figure 2. Screen snip of the formula tool.   If this is still not enough, double-click on the equations (inside the blue rectangle) to activate the encoding mode. Shuran_2-1705892812448.png Figure 3. Where to find the code editor.   For a complete guide to using formulas to get the most from your data, view the webinars here: Using Formulas to Get the Most from Your Data - JMP User Community   The only limit is your imagination. I hope you enjoy using JMP. Shuran, Customer Success at JMP  ... View more